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        <script src="/assets/js/APlayer.min.js"> </script><p>学习一下dl，哪怕是概念呢。本文是转的。为何要转？怕丢失了呗，哈哈哈哈</p>
<a id="more"></a>  
<p>这篇文章是转的</p>
<blockquote>
<p><a href="http://www.360doc.com/content/18/0527/23/48630098_757540268.shtml" target="_blank" rel="noopener">http://www.360doc.com/content/18/0527/23/48630098_757540268.shtml</a></p>
</blockquote>
<img src="/2019/2019-07-30-learn-dl-for-a-noob/01.jpg">
<h1 id="怎么入门机器-深度学习？"><a href="#怎么入门机器-深度学习？" class="headerlink" title="怎么入门机器/深度学习？"></a>怎么入门机器/深度学习？</h1><p>回答这个问题，最先要考虑的问题是：你有多少时间？</p>
<p>准备用三个月入门，和想要一个月速成，肯定是截然不同的路径。当然我建议大家稳扎稳打，至少可以拿出五个月的时间来学好机器学习的基础知识。</p>
<p>基础很重要，知其所以然很重要。毕竟工具总在进步，每个月都会出现更好的深度学习技术，但基础知识是不变的。</p>
<p>如何用五个月时间入门？下面分三个部分，详细指南。（以及，如果你确实时间有限，最后还有一个速成指南）</p>
<h1 id="五个月入门"><a href="#五个月入门" class="headerlink" title="五个月入门"></a>五个月入门</h1><h2 id="Part-1：从机器学习开始（两个月）"><a href="#Part-1：从机器学习开始（两个月）" class="headerlink" title="Part 1：从机器学习开始（两个月）"></a>Part 1：从机器学习开始（两个月）</h2><p>最好的入门教程，就是吴恩达讲授的机器学习。吴恩达这套课程发布很久了，虽然有些地方稍微过时，但相信我，现在没有任何公开的课程，能比吴恩达讲得更好。<br>真的，课程结束时我几乎哭了出来。</p>
<p>这个课程可以说适合任何水平的学生，当然，你最好还是得知道两个矩阵如何相乘，以及对编程有一些基本的了解。</p>
<img src="/2019/2019-07-30-learn-dl-for-a-noob/02.jpg">
<p>这套课程可以前往Coursera学习，传送门：</p>
<blockquote>
<p><a href="https://www.coursera.org/learn/machine-learning" target="_blank" rel="noopener">https://www.coursera.org/learn/machine-learning</a></p>
</blockquote>
<p>也可以上网易公开课收看，传送门：</p>
<blockquote>
<p><a href="http://open.163.com/special/opencourse/machinelearning.html" target="_blank" rel="noopener">http://open.163.com/special/opencourse/machinelearning.html</a></p>
</blockquote>
<p>如果你有时间，一定要听完全部的课程。如果时间紧张，至少要听完前五节课程，后面的可以暂时跳过。</p>
<p>吴恩达的机器学习课程深入讲解了经典的机器学习模型，如线性回归、逻辑回归、神经网络、支持向量机、PCA、无监督学习等等。<br>大部分重要概念，都以简单易懂的方式进行了介绍。</p>
<p><strong>课程延伸</strong></p>
<p>当你学习到第五节课，也就是开始讲述神经网络时，建议开始查看与课程平行的外部资料。比方3bule1brown推出的神经网络讲解视频。推荐必看。</p>
<p>YouTube传送门：</p>
<blockquote>
<p><a href="https://youtu.be/aircAruvnKk?list=PLZHQObOWTQDNU6R1_67000Dx_ZCJB-3pi" target="_blank" rel="noopener">https://youtu.be/aircAruvnKk?list=PLZHQObOWTQDNU6R1_67000Dx_ZCJB-3pi</a></p>
</blockquote>
<p>或者可以前往B站查看：</p>
<blockquote>
<p><a href="http://space.bilibili.com/88461692/#/" target="_blank" rel="noopener">http://space.bilibili.com/88461692/#/</a></p>
</blockquote>
<p>这里给个示例：</p>
<p>以及，我觉得吴恩达在讲神经网络时有点快，所以建议补充阅读一些资料。比如有关神经网络和深度学习的在线书籍，免费的就很好了。</p>
<p>传送门：</p>
<blockquote>
<p><a href="http://neuralnetworksanddeeplearning.com/" target="_blank" rel="noopener">http://neuralnetworksanddeeplearning.com/</a></p>
</blockquote>
<img src="/2019/2019-07-30-learn-dl-for-a-noob/03.jpg">
<p>作者Michael A. Nielsen以一种简单直观的方式，深入探究了神经网络的每个细节。建议阅读这本书的前两章，与吴恩达的课程并行。当你熟悉更多概念后，开始搞深度学习时，可以再看书中的其余部分。<br>如果你英文不好，这本《神经网络与深度学习》也有中文翻译版本，可以免费在线查看。<br>感谢译者，传送门在此：</p>
<blockquote>
<p><a href="https://tigerneil.gitbooks.io/neural-networks-and-deep-learning-zh/content/" target="_blank" rel="noopener">https://tigerneil.gitbooks.io/neural-networks-and-deep-learning-zh/content/</a></p>
</blockquote>
<img src="/2019/2019-07-30-learn-dl-for-a-noob/04.jpg">
<p>这个部分的学习结束之后，你就能明白机器/深度学习的许多概念。最后推荐阅读Christopher Olah的博客，很有意思。</p>
<p>传送门：</p>
<blockquote>
<p><a href="http://colah.github.io/" target="_blank" rel="noopener">http://colah.github.io/</a></p>
</blockquote>
<img src="/2019/2019-07-30-learn-dl-for-a-noob/05.jpg">
<h2 id="Part-2：涉足深度学习（1个月）"><a href="#Part-2：涉足深度学习（1个月）" class="headerlink" title="Part 2：涉足深度学习（1个月）"></a>Part 2：涉足深度学习（1个月）</h2><p>开始研究深度学习之前，最好重温一下大学数学。Ian Goodfellow传奇般的“花书”《深度学习》，简明扼要的概括了大部分重要主题。</p>
<p>建议大家尽可能深入地阅读线性代数、概率、信息理论的章节。每当读论文遇到深度学习概念时，都可以在书中找到参考。</p>
<img src="/2019/2019-07-30-learn-dl-for-a-noob/06.jpg">
<p>以及，这本书有在线的版本。</p>
<p>例如英文版在此：</p>
<blockquote>
<p><a href="https://github.com/janishar/mit-deep-learning-book-pdf/blob/master/complete-book-bookmarked-pdf/deeplearningbook.pdf" target="_blank" rel="noopener">https://github.com/janishar/mit-deep-learning-book-pdf/blob/master/complete-book-bookmarked-pdf/deeplearningbook.pdf</a></p>
</blockquote>
<p>而中文翻译版本在此：</p>
<blockquote>
<p><a href="https://github.com/exacity/deeplearningbook-chinese" target="_blank" rel="noopener">https://github.com/exacity/deeplearningbook-chinese</a></p>
</blockquote>
<img src="/2019/2019-07-30-learn-dl-for-a-noob/07.jpg">
<p>关于深度学习的在线资料有很多，你可能会挑花了眼。</p>
<p>再一次，我觉得最好的选择，还是听吴恩达的《深度学习专项系列课程（Deep Learning Specialization）》。</p>
<p>Coursera传送门：</p>
<blockquote>
<p><a href="https://www.coursera.org/specializations/deep-learning" target="_blank" rel="noopener">https://www.coursera.org/specializations/deep-learning</a></p>
</blockquote>
<p>网易云课堂的传送门：</p>
<blockquote>
<p><a href="https://mooc.study.163.com/smartSpec/detail/1001319001.htm/" target="_blank" rel="noopener">https://mooc.study.163.com/smartSpec/detail/1001319001.htm/</a></p>
</blockquote>
<p>这门课程包括五大章节。其实不是免费的，你可以按照50美元/月购买。当然，如果你负担不起，还能申请“助学金”。申请时请详细阐明理由，处理的时间大概需要15天左右。</p>
<p>当然不付费，大部分内容都是可以看的。以及视频的部分，在很多地方也能免费收看。</p>
<p>这五门课程主要讲的是：</p>
<ol>
<li>神经网络和深度学习（4周）</li>
<li>改善深度神经网络（3周）</li>
<li>结构化机器学习项目（2周）</li>
<li>卷积神经网络（4周）</li>
<li>序列模型（3周）</li>
</ol>
<img src="/2019/2019-07-30-learn-dl-for-a-noob/08.jpg">
<p>前三门课程涉及一般的神经网络和深度学习，第四、第五门课程涉及特定主题。如果你打算搞视觉，第四课必听；<br>如果你搞NLP、音频等，第五课必听。但如果你需要听第五课，那么建议也把第四课好好听一下。</p>
<p>这里鼓励大家一下，课程里每周的内容，实际上一两天就能学完，所以不要被课程表吓倒。劳逸集合、提升效率。</p>
<p>学到这个地步，其实就可以再去<a href="http://neuralnetworksanddeeplearning.com/" target="_blank" rel="noopener">http://neuralnetworksanddeeplearning.com/</a> ，查看第三到第六章的内容，来强化你的概念。<br>如果你有什么还没搞懂的，请前往Olah的博客。</p>
<p>以及，这时候你要开始看深度学习的论文了，从中学习知识。深度学习有个强烈的特点，那就是内容都非常新，阅读论文是跟上时代唯一的方法。<br>不想被抛下，那么还是养成阅读论文的好习惯吧。</p>
<h2 id="Part-3：深度学习上手练（两个月）"><a href="#Part-3：深度学习上手练（两个月）" class="headerlink" title="Part 3：深度学习上手练（两个月）"></a>Part 3：深度学习上手练（两个月）</h2><p>学到这里，你应该对机器学习和深度学习中的大多数概念有了正确的理解，现在是时候投入沸腾的实际生活中了。</p>
<p>练手深度学习，最好的资源在fast.ai。</p>
<img src="/2019/2019-07-30-learn-dl-for-a-noob/09.jpg">
<p>传送门在此：</p>
<blockquote>
<p><a href="http://course.fast.ai/" target="_blank" rel="noopener">http://course.fast.ai/</a></p>
</blockquote>
<p>他们在流行的深度学习工具PyTorch上构建了一个库，只需要几行代码，就能实现世界级的性能。</p>
<p>fast.ai的理念有点不同。吴恩达等老师的教授方法是自上而下，先讲再做。而fast.ai倡导自下而上，先做再讲。</p>
<p>所以在他们的课程中，第一节就带你建立一个强大的图像分类器。自己训练模型的快感，刺激着你去完成其余的课程。</p>
<img src="/2019/2019-07-30-learn-dl-for-a-noob/10.jpg">
<p>除此以外，还要推荐两门课。</p>
<p>斯坦福大学的CS231n和CS224n。CS231n专注于计算机视觉的深度学习，而CS224n专注于序列建模。</p>
<p>CS231n，李飞飞等主讲。</p>
<p>官网传送门：</p>
<blockquote>
<p><a href="http://cs231n.stanford.edu/" target="_blank" rel="noopener">http://cs231n.stanford.edu/</a></p>
</blockquote>
<p>CS224n，目前是Richard Socher主讲。<br>官网传送门：</p>
<blockquote>
<p><a href="http://web.stanford.edu/class/cs224n/" target="_blank" rel="noopener">http://web.stanford.edu/class/cs224n/</a></p>
</blockquote>
<p>此前的课程，网上也有中文字幕版本，大家可自行搜索。</p>
<p>到这里，为期五个月的机器/深度学习入门就结束了。</p>
<p>希望大家都能稳扎稳打，夯实基础。</p>
<p>以及最后，兑现一个开头的承诺。如果你确实时间很紧张，必须尽快入门机器/深度学习，那么请看——</p>
<h1 id="速成指南"><a href="#速成指南" class="headerlink" title="速成指南"></a>速成指南</h1><h2 id="我最多只有俩月"><a href="#我最多只有俩月" class="headerlink" title="我最多只有俩月"></a>我最多只有俩月</h2><ol>
<li>完成吴恩达机器学习课程的前五周，要做编程练习。</li>
<li>看完3Blue1Brown的视频。</li>
<li>完成吴恩达的深度学习专项系列课程，做练习。</li>
<li>如果你想搞图像，看专项课程第四讲，搞NLP或序列数据，看第五讲。</li>
<li>搜索你感兴趣的开源实现。如果你还没想好用什么语言，推荐Keras。然后根据需要，再迁到TensorFlow或者PyTorch框架。</li>
</ol>
<h2 id="我只有一个月"><a href="#我只有一个月" class="headerlink" title="我只有一个月"></a>我只有一个月</h2><p>想要在30天完成入门超级困难。除非，你只是想了解机器学习的工作原理，然后应用到自己的项目中。</p>
<p>如果是这样的话，速成建议如下：</p>
<ol>
<li>略读吴恩达机器学习课程第1-5周的课程，只看视频，掌握概念即可。第三周可以跳过MATLAB/Octave课程。</li>
<li>看完3Blue1Brow的视频。</li>
<li>略读吴恩达深度学习专项系列课程的第一课，也就是神经网络和深度学习。</li>
<li>如果你想做图像处理项目，看一下Nielsen书中的第六章：<a href="http://neuralnetworksanddeeplearning.com/chap6.html" target="_blank" rel="noopener">http://neuralnetworksanddeeplearning.com/chap6.html</a><br> 如果你需要序列建模的一些想法，可以看看Olah的博客：<a href="http://colah.github.io/posts/2015-08-Understanding-LSTMs/" target="_blank" rel="noopener">http://colah.github.io/posts/2015-08-Understanding-LSTMs/</a><br>5、Siraj Raval拍了很多有趣的视频，涉及大多数机器/深度学习的主题。传送门在此：<a href="https://www.youtube.com/channel/UCWN3xxRkmTPmbKwht9FuE5A" target="_blank" rel="noopener">https://www.youtube.com/channel/UCWN3xxRkmTPmbKwht9FuE5A</a><br>6、搜索跟你感兴趣的开源实现，随时调整以满足你的需求。如前所述，我推荐你先用带有TensorFlow后端的Keras语言。</li>
</ol>
<h1 id="其他资源"><a href="#其他资源" class="headerlink" title="其他资源"></a>其他资源</h1><p>YouTube上有一个两分钟读论文的系列视频，可以帮你快速了解全球深度学习的最热门进展。</p>
<p>如果你关注进机器学习领域的进展，Twitter是个绝佳的工具。</p>
<p>遇到困境的时候，记得reddit和Facebook上有很多志同道合的人，不要犹豫，在社区里寻求帮助，大家会伸出援手。</p>
<h1 id="结论"><a href="#结论" class="headerlink" title="结论"></a>结论</h1><p>机器学习和深度学习是当今世界最具魅力的技术之一。而且这个领域的深度学习专家总是处于稀缺的状态。从职业前景来看，深度学习非常吸引人。</p>
<p>需要提醒的是，与计算机学科的其他领域不同，深度学习的资源还不够丰富。很多时候你会遇到失败挫折，千万不要灰心丧气，<br>你可以向更多人寻求帮助，很多人都愿意伸出援手，大家都在学习。</p>
<p>关于机器/深度学习，有一个误解是需要计算机科学的背景才能学习。这不是真的，你确实需要一些编程的思维才好入手，但也仅限于此。<br>现在机器学习领域的很多专家，都来自其他研究领域。</p>
<p>如果你有计算机科学的背景，这是一个非常好的开始。但如果你出身其他学科，想要迎头赶上并不难。</p>
<p>感谢看到这里。</p>
<img src="/2019/2019-07-30-learn-dl-for-a-noob/11.jpg">
<p>— 完 —</p>

      
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              <div class="post-toc-content"><ol class="nav"><li class="nav-item nav-level-1"><a class="nav-link" href="#怎么入门机器-深度学习？"><span class="nav-number">1.</span> <span class="nav-text">怎么入门机器/深度学习？</span></a></li><li class="nav-item nav-level-1"><a class="nav-link" href="#五个月入门"><span class="nav-number">2.</span> <span class="nav-text">五个月入门</span></a><ol class="nav-child"><li class="nav-item nav-level-2"><a class="nav-link" href="#Part-1：从机器学习开始（两个月）"><span class="nav-number">2.1.</span> <span class="nav-text">Part 1：从机器学习开始（两个月）</span></a></li><li class="nav-item nav-level-2"><a class="nav-link" href="#Part-2：涉足深度学习（1个月）"><span class="nav-number">2.2.</span> <span class="nav-text">Part 2：涉足深度学习（1个月）</span></a></li><li class="nav-item nav-level-2"><a class="nav-link" href="#Part-3：深度学习上手练（两个月）"><span class="nav-number">2.3.</span> <span class="nav-text">Part 3：深度学习上手练（两个月）</span></a></li></ol></li><li class="nav-item nav-level-1"><a class="nav-link" href="#速成指南"><span class="nav-number">3.</span> <span class="nav-text">速成指南</span></a><ol class="nav-child"><li class="nav-item nav-level-2"><a class="nav-link" href="#我最多只有俩月"><span class="nav-number">3.1.</span> <span class="nav-text">我最多只有俩月</span></a></li><li class="nav-item nav-level-2"><a class="nav-link" href="#我只有一个月"><span class="nav-number">3.2.</span> <span class="nav-text">我只有一个月</span></a></li></ol></li><li class="nav-item nav-level-1"><a class="nav-link" href="#其他资源"><span class="nav-number">4.</span> <span class="nav-text">其他资源</span></a></li><li class="nav-item nav-level-1"><a class="nav-link" href="#结论"><span class="nav-number">5.</span> <span class="nav-text">结论</span></a></li></ol></div>
            

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  <script type="text/javascript">
    // Popup Window;
    var isfetched = false;
    var isXml = true;
    // Search DB path;
    var search_path = "search.xml";
    if (search_path.length === 0) {
      search_path = "search.xml";
    } else if (/json$/i.test(search_path)) {
      isXml = false;
    }
    var path = "/" + search_path;
    // monitor main search box;

    var onPopupClose = function (e) {
      $('.popup').hide();
      $('#local-search-input').val('');
      $('.search-result-list').remove();
      $('#no-result').remove();
      $(".local-search-pop-overlay").remove();
      $('body').css('overflow', '');
    }

    function proceedsearch() {
      $("body")
        .append('<div class="search-popup-overlay local-search-pop-overlay"></div>')
        .css('overflow', 'hidden');
      $('.search-popup-overlay').click(onPopupClose);
      $('.popup').toggle();
      var $localSearchInput = $('#local-search-input');
      $localSearchInput.attr("autocapitalize", "none");
      $localSearchInput.attr("autocorrect", "off");
      $localSearchInput.focus();
    }

    // search function;
    var searchFunc = function(path, search_id, content_id) {
      'use strict';

      // start loading animation
      $("body")
        .append('<div class="search-popup-overlay local-search-pop-overlay">' +
          '<div id="search-loading-icon">' +
          '<i class="fa fa-spinner fa-pulse fa-5x fa-fw"></i>' +
          '</div>' +
          '</div>')
        .css('overflow', 'hidden');
      $("#search-loading-icon").css('margin', '20% auto 0 auto').css('text-align', 'center');

      $.ajax({
        url: path,
        dataType: isXml ? "xml" : "json",
        async: true,
        success: function(res) {
          // get the contents from search data
          isfetched = true;
          $('.popup').detach().appendTo('.header-inner');
          var datas = isXml ? $("entry", res).map(function() {
            return {
              title: $("title", this).text(),
              content: $("content",this).text(),
              url: $("url" , this).text()
            };
          }).get() : res;
          var input = document.getElementById(search_id);
          var resultContent = document.getElementById(content_id);
          var inputEventFunction = function() {
            var searchText = input.value.trim().toLowerCase();
            var keywords = searchText.split(/[\s\-]+/);
            if (keywords.length > 1) {
              keywords.push(searchText);
            }
            var resultItems = [];
            if (searchText.length > 0) {
              // perform local searching
              datas.forEach(function(data) {
                var isMatch = false;
                var hitCount = 0;
                var searchTextCount = 0;
                var title = data.title.trim();
                var titleInLowerCase = title.toLowerCase();
                var content = data.content.trim().replace(/<[^>]+>/g,"");
                var contentInLowerCase = content.toLowerCase();
                var articleUrl = decodeURIComponent(data.url);
                var indexOfTitle = [];
                var indexOfContent = [];
                // only match articles with not empty titles
                if(title != '') {
                  keywords.forEach(function(keyword) {
                    function getIndexByWord(word, text, caseSensitive) {
                      var wordLen = word.length;
                      if (wordLen === 0) {
                        return [];
                      }
                      var startPosition = 0, position = [], index = [];
                      if (!caseSensitive) {
                        text = text.toLowerCase();
                        word = word.toLowerCase();
                      }
                      while ((position = text.indexOf(word, startPosition)) > -1) {
                        index.push({position: position, word: word});
                        startPosition = position + wordLen;
                      }
                      return index;
                    }

                    indexOfTitle = indexOfTitle.concat(getIndexByWord(keyword, titleInLowerCase, false));
                    indexOfContent = indexOfContent.concat(getIndexByWord(keyword, contentInLowerCase, false));
                  });
                  if (indexOfTitle.length > 0 || indexOfContent.length > 0) {
                    isMatch = true;
                    hitCount = indexOfTitle.length + indexOfContent.length;
                  }
                }

                // show search results

                if (isMatch) {
                  // sort index by position of keyword

                  [indexOfTitle, indexOfContent].forEach(function (index) {
                    index.sort(function (itemLeft, itemRight) {
                      if (itemRight.position !== itemLeft.position) {
                        return itemRight.position - itemLeft.position;
                      } else {
                        return itemLeft.word.length - itemRight.word.length;
                      }
                    });
                  });

                  // merge hits into slices

                  function mergeIntoSlice(text, start, end, index) {
                    var item = index[index.length - 1];
                    var position = item.position;
                    var word = item.word;
                    var hits = [];
                    var searchTextCountInSlice = 0;
                    while (position + word.length <= end && index.length != 0) {
                      if (word === searchText) {
                        searchTextCountInSlice++;
                      }
                      hits.push({position: position, length: word.length});
                      var wordEnd = position + word.length;

                      // move to next position of hit

                      index.pop();
                      while (index.length != 0) {
                        item = index[index.length - 1];
                        position = item.position;
                        word = item.word;
                        if (wordEnd > position) {
                          index.pop();
                        } else {
                          break;
                        }
                      }
                    }
                    searchTextCount += searchTextCountInSlice;
                    return {
                      hits: hits,
                      start: start,
                      end: end,
                      searchTextCount: searchTextCountInSlice
                    };
                  }

                  var slicesOfTitle = [];
                  if (indexOfTitle.length != 0) {
                    slicesOfTitle.push(mergeIntoSlice(title, 0, title.length, indexOfTitle));
                  }

                  var slicesOfContent = [];
                  while (indexOfContent.length != 0) {
                    var item = indexOfContent[indexOfContent.length - 1];
                    var position = item.position;
                    var word = item.word;
                    // cut out 100 characters
                    var start = position - 20;
                    var end = position + 80;
                    if(start < 0){
                      start = 0;
                    }
                    if (end < position + word.length) {
                      end = position + word.length;
                    }
                    if(end > content.length){
                      end = content.length;
                    }
                    slicesOfContent.push(mergeIntoSlice(content, start, end, indexOfContent));
                  }

                  // sort slices in content by search text's count and hits' count

                  slicesOfContent.sort(function (sliceLeft, sliceRight) {
                    if (sliceLeft.searchTextCount !== sliceRight.searchTextCount) {
                      return sliceRight.searchTextCount - sliceLeft.searchTextCount;
                    } else if (sliceLeft.hits.length !== sliceRight.hits.length) {
                      return sliceRight.hits.length - sliceLeft.hits.length;
                    } else {
                      return sliceLeft.start - sliceRight.start;
                    }
                  });

                  // select top N slices in content

                  var upperBound = parseInt('1');
                  if (upperBound >= 0) {
                    slicesOfContent = slicesOfContent.slice(0, upperBound);
                  }

                  // highlight title and content

                  function highlightKeyword(text, slice) {
                    var result = '';
                    var prevEnd = slice.start;
                    slice.hits.forEach(function (hit) {
                      result += text.substring(prevEnd, hit.position);
                      var end = hit.position + hit.length;
                      result += '<b class="search-keyword">' + text.substring(hit.position, end) + '</b>';
                      prevEnd = end;
                    });
                    result += text.substring(prevEnd, slice.end);
                    return result;
                  }

                  var resultItem = '';

                  if (slicesOfTitle.length != 0) {
                    resultItem += "<li><a href='" + articleUrl + "' class='search-result-title'>" + highlightKeyword(title, slicesOfTitle[0]) + "</a>";
                  } else {
                    resultItem += "<li><a href='" + articleUrl + "' class='search-result-title'>" + title + "</a>";
                  }

                  slicesOfContent.forEach(function (slice) {
                    resultItem += "<a href='" + articleUrl + "'>" +
                      "<p class=\"search-result\">" + highlightKeyword(content, slice) +
                      "...</p>" + "</a>";
                  });

                  resultItem += "</li>";
                  resultItems.push({
                    item: resultItem,
                    searchTextCount: searchTextCount,
                    hitCount: hitCount,
                    id: resultItems.length
                  });
                }
              })
            };
            if (keywords.length === 1 && keywords[0] === "") {
              resultContent.innerHTML = '<div id="no-result"><i class="fa fa-search fa-5x" /></div>'
            } else if (resultItems.length === 0) {
              resultContent.innerHTML = '<div id="no-result"><i class="fa fa-frown-o fa-5x" /></div>'
            } else {
              resultItems.sort(function (resultLeft, resultRight) {
                if (resultLeft.searchTextCount !== resultRight.searchTextCount) {
                  return resultRight.searchTextCount - resultLeft.searchTextCount;
                } else if (resultLeft.hitCount !== resultRight.hitCount) {
                  return resultRight.hitCount - resultLeft.hitCount;
                } else {
                  return resultRight.id - resultLeft.id;
                }
              });
              var searchResultList = '<ul class=\"search-result-list\">';
              resultItems.forEach(function (result) {
                searchResultList += result.item;
              })
              searchResultList += "</ul>";
              resultContent.innerHTML = searchResultList;
            }
          }

          if ('auto' === 'auto') {
            input.addEventListener('input', inputEventFunction);
          } else {
            $('.search-icon').click(inputEventFunction);
            input.addEventListener('keypress', function (event) {
              if (event.keyCode === 13) {
                inputEventFunction();
              }
            });
          }

          // remove loading animation
          $(".local-search-pop-overlay").remove();
          $('body').css('overflow', '');

          proceedsearch();
        }
      });
    }

    // handle and trigger popup window;
    $('.popup-trigger').click(function(e) {
      e.stopPropagation();
      if (isfetched === false) {
        searchFunc(path, 'local-search-input', 'local-search-result');
      } else {
        proceedsearch();
      };
    });

    $('.popup-btn-close').click(onPopupClose);
    $('.popup').click(function(e){
      e.stopPropagation();
    });
    $(document).on('keyup', function (event) {
      var shouldDismissSearchPopup = event.which === 27 &&
        $('.search-popup').is(':visible');
      if (shouldDismissSearchPopup) {
        onPopupClose();
      }
    });
  </script>





  

  
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  <script>AV.initialize("", "");</script>
  <script>
    function showTime(Counter) {
      var query = new AV.Query(Counter);
      var entries = [];
      var $visitors = $(".leancloud_visitors");

      $visitors.each(function () {
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          console.log("Error: " + error.code + " " + error.message);
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    }

    function addCount(Counter) {
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      var url = $visitors.attr('id').trim();
      var title = $visitors.attr('data-flag-title').trim();
      var query = new AV.Query(Counter);

      query.equalTo("url", url);
      query.find({
        success: function(results) {
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            counter.fetchWhenSave(true);
            counter.increment("time");
            counter.save(null, {
              success: function(counter) {
                var $element = $(document.getElementById(url));
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            newcounter.set("title", title);
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            newcounter.set("time", 1);
            newcounter.save(null, {
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                var $element = $(document.getElementById(url));
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    $(function() {
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      } else if ($('.post-title-link').length > 1) {
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    });
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